the proceedings contain 23 papers. the special focus in this conference is on Advanced Informatics for Computing Research. the topics include: Efficient Entity Resolution for Bibliographic data Using MapReduce;se...
ISBN:
(纸本)9783031094682
the proceedings contain 23 papers. the special focus in this conference is on Advanced Informatics for Computing Research. the topics include: Efficient Entity Resolution for Bibliographic data Using MapReduce;securing of Identification System data Transmission Using Deep Autoencoders and data Hiding;a Hybrid Filter/Wrapper machinelearning Model for Classification Cancer dataset;edge-Assisted IoT Architecture: A Case of Air Pollution Monitoring Frameworks;optimized Analysis Using Feature Selection Techniques for Drug Discovery Detection;crop Identification and Disease Detection by Using Convolutional Neural Networks;an Efficient Novel Approach for Detection of Handwritten Numericals Using machinelearning Paradigms;multiclass Classification in machinelearning Algorithms for Disease Prediction;CREA-Components Reusability Evaluation and Assessment: An Algorithmic Perspective;capsule Network Based Speech Emotion recognition for Efficient Capturing of Spatial Features;a Survey and Analysis on the Distraction patterns of the Students in E-learning and M-learning Environments;relationships Among Human Genome Graph Elements Using Clusters Detection;transfer learning Architecture Approach for Smart Transportation System;preface;a Blockchain-Based Solution for Electronic Medical Records System in Healthcare;analysing Sentiments of People Over Vaccines in Reddit Posts Using Natural Language Processing;segmentation of Tumor Region from Mammogram Images Using Deep learning Approach;review on IoT Based Real-Time Healthcare Monitoring System;application of IoT in 5G Wireless Communication: A Detailed Review;enhanced Service Point Approach for Microservices Based Applications Using machinelearning Techniques;Worldwide Vaccination Report for COVID-19 Analysis and Visualization Using Deep learning.
A GAN-based image recognition algorithm is presented to solve these problems. Firstly, the GAN frame is composed of a generator and a discriminator. the generator can produce real images or remove noise by learningth...
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ISBN:
(数字)9798331533694
ISBN:
(纸本)9798331533700
A GAN-based image recognition algorithm is presented to solve these problems. Firstly, the GAN frame is composed of a generator and a discriminator. the generator can produce real images or remove noise by learningthe signal image. then, the image is compared withthe real image. In the pre-processing phase, the standard, denotation and enhancement of the signal image are carried out to guarantee the data quality and the validity of the characteristic. Experiments show that the precision of GAN-based image recognition is up to 96.3%, which is more than 15% than traditional methods (e.g., SVM). In the signal denoising task, the model significantly improves the signal-to-noise ratio (SNR) by 20% and effectively removes image noise. In addition, GAN also performs extremely well in patternrecognition tasks, with a correct recognition rate of 94.7%. Compared with traditional deep learning algorithms, the GAN model shows stronger robustness when processing signals with more noise or incomplete data.
Withthe rapid pace of urbanization, the demand for urban public green space is becoming increasingly evident, and pocket parks are emerging as an ideal solution in high-density urban environments due to their notable...
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ISBN:
(数字)9798350355925
ISBN:
(纸本)9798350355932
Withthe rapid pace of urbanization, the demand for urban public green space is becoming increasingly evident, and pocket parks are emerging as an ideal solution in high-density urban environments due to their notable advantages, including small footprint, diverse forms, and flexible functions. However, traditional pocket park design processes face challenges such as short design cycles and time-consuming multi-program conceptualization. In response, this paper proposes an automatic generation method for pocket park layout plans based on generative adversarial networks (GANs). By constructing a training dataset consisting of 119 pairs of finely labeled images, a model capable of generating pocket park layout plans is successfully trained. the performance of the model is comprehensively and meticulously evaluated from both quantitative and qualitative perspectives to validate the feasibility and efficiency of the method. Experimental results show that the proposed method rapidly generates a large number of pocket park layouts with high design quality and innovation, significantly improving design efficiency.
In the context of social media, the automatic identification of sarcastic discourse presents a complex challenge within the field of natural language processing. Due to the significant disparity between the literal an...
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ISBN:
(数字)9798350355925
ISBN:
(纸本)9798350355932
In the context of social media, the automatic identification of sarcastic discourse presents a complex challenge within the field of natural language processing. Due to the significant disparity between the literal and implied meanings of sarcastic statements, traditional text analysis algorithms struggle to accurately detect them. this paper introduces a method for detecting Chinese sarcastic comments on social media, which integrates an improved cross-entropy loss function with regularization techniques. the method employs a dynamic adjustment factor to optimize weighted cross-entropy loss function, calibrating the model's penalty intensity for high-confidence predictions, and enhances the consistency of the model's output through batch-averaged Kullback-Leibler divergence. Experiments were conducted using the Chinese-RoBERTa model as a baseline, validated on full data and small sample sizes (50%, 10%). the results demonstrate that, compared to the baseline model, the proposed method achieved accuracy improvements of 3.7%, 2.5%, and 2.3% across different data volumes, confirming its effectiveness and applicability in the detection of Chinese sarcastic discourse.
Deep learning-based object detection in remote sensing images is an important yet challenging task because of the complex background and large variations in size of the targets. Currently, many detectors have made sig...
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this paper employs an innovative approach that combines self-supervised learning feature fusion and adapter fine-tuning for a speech emotion recognition task. the results show a 5.57% improvement in accuracy compared ...
this paper employs an innovative approach that combines self-supervised learning feature fusion and adapter fine-tuning for a speech emotion recognition task. the results show a 5.57% improvement in accuracy compared to the baseline (Hubert upstream model) in the speech emotion recognition task, reaching the highest level of 75.1%. this progress illustrates the significant improvement of adapter fine-tuning on the migration performance of self-supervised learning features in different tasks. In summary, this study introduces an innovative solution to address the discrepancy between self-supervised learning features and downstream tasks, offering valuable insights for enhancing performance in speech emotion recognition. the integration of self-supervised learning with adapter fine-tuning not only holds promise for achieving comparable success across diverse speech-related tasks but also contributes fresh perspectives to the advancement of speech processing.
Withthe development of the automobile industry, the use of vehicles has become the most basic means of transportation in people’s daily life. However, due to the rapid increase in the number of vehicles, more and mo...
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datamining is a useful method for extracting meaningful information from massive volumes of records. Statistics and machinelearning may be utilised to evaluate large-scale patterns in massive databases. Numerous res...
datamining is a useful method for extracting meaningful information from massive volumes of records. Statistics and machinelearning may be utilised to evaluate large-scale patterns in massive databases. Numerous researchers use image processing methods to predict disease, utilising datamining and machinelearning approaches. Disease data can be collected from various sources. A bioinformatics technology will be used for keeping, retrieving, and interpreting information with biological molecules. A forecast can be explained as a description of upcoming events based on current conditions. the aim of this study is to create machinelearning and datamining algorithms for diabetes prediction. there are various steps in predicting diabetes. In this study, a vote-based classifier is developed to predict diabetes. the proposed algorithm, is used to optimize the diabetes prediction performance up to 2%.
the proceedings contain 13 papers. the special focus in this conference is on Smart Grid and Internet of things. the topics include: Big data Grave Register Information Management System Outside Cemeteries Under Inter...
ISBN:
(纸本)9783031203978
the proceedings contain 13 papers. the special focus in this conference is on Smart Grid and Internet of things. the topics include: Big data Grave Register Information Management System Outside Cemeteries Under Internet of things;critical Feature Selection and machinelearning-based Models for Monofacial and Bifacial Photovoltaics;Study on the Discovery of Personal Information on the Network of People Diagnosed with COVID-19;China IoT UBI Car Insurance Regulatory Development Trend;constructing a Violence recognition Technique for Elderly Patients with Lower Limb Disability;aquaculture Monitoring Systems Based on Lightweight Kubernetes and Rancher;Design and Implementation of Water Monitoring Using LoRa and NB-IoT;interWorking Function for Mission Critical Push to Talk Services;lightweight Privacy-Preserving data Aggregation Scheme Based on Elliptic Curve Cryptography for Smart Grid Communications;design and Implementation of Distributed Image recognition App with Federal learning Techniques;improving Vision Clarity and Object Detection Accuracy in Heavy Rain Base on Neural Network.
Nonverbal communication plays a vital role in human interaction. In the context of Human-Robot Interaction (HRI), social robots are designed primarily for verbal-based communication with humans, making nonverbal commu...
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ISBN:
(纸本)9783031486418;9783031486425
Nonverbal communication plays a vital role in human interaction. In the context of Human-Robot Interaction (HRI), social robots are designed primarily for verbal-based communication with humans, making nonverbal communication an open research area. We present a flexible, open framework designed to facilitate nonverbal interactions in HRI. Among its components is a P2P Browse-rBased Computational Notebook, leveraged to code, run, and share reactive programs. machine-learning models can be included for real-time recognition of gestures, poses, and moods, employing protocols such as MQTT. Another key component is a broker for distributing data among different physical devices like the robot, wearables, and environmental sensors. We demonstrate this framework's utility through two interaction scenarios: (i) the first one employing proxemics and gaze direction to initiate an impromptu encounter, and (ii) a second one incorporating object recognition and a Large-Language Model to suggest meals to be cooked based on available ingredients. these scenarios illustrate how the framework's components can be seamlessly integrated to address new scenarios, where robots need to infer nonverbal cues from users.
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